利用人工智能（AI）对医学图像中的肝脏结构进行分割已成为过去五年的热门研究热点。人工智能工具在筛选此任务方面的性能可能差异很大，并且已在各种数据集中的文献中进行了测试。然而，还没有科学计量学报告对该科学领域提供系统的概述。本文对神经网络建模方法（主要是深度学习）的最新进展进行了系统的文献计量回顾，概述了该领域在算法特征方面的多个研究方向。因此，本文从算法建模目标、性能基准和模型复杂性方面对计算机断层扫描 (CT) 图像中全自动语义分割肝脏结构的最相关出版物进行了详细的系统回顾。该评论表明，全自动混合 2D 和 3D 网络在肝脏语义分割中表现最佳。在肝脏肿瘤和脉管系统分割的情况下，全自动生成方法表现最佳。然而，报告的性能基准表明，在高分辨率腹部 CT 扫描中分割如此小的结构仍有很多需要改进的地方。© 2024。作者。

The use of artificial intelligence (AI) in the segmentation of liver structures in medical images has become a popular research focus in the past half-decade. The performance of AI tools in screening for this task may vary widely and has been tested in the literature in various datasets. However, no scientometric report has provided a systematic overview of this scientific area. This article presents a systematic and bibliometric review of recent advances in neuronal network modeling approaches, mainly of deep learning, to outline the multiple research directions of the field in terms of algorithmic features. Therefore, a detailed systematic review of the most relevant publications addressing fully automatic semantic segmenting liver structures in Computed Tomography (CT) images in terms of algorithm modeling objective, performance benchmark, and model complexity is provided. The review suggests that fully automatic hybrid 2D and 3D networks are the top performers in the semantic segmentation of the liver. In the case of liver tumor and vasculature segmentation, fully automatic generative approaches perform best. However, the reported performance benchmark indicates that there is still much to be improved in segmenting such small structures in high-resolution abdominal CT scans.© 2024. The Author(s).